As someone who has also struggled with similar issues, although in a different context than writing papers, I found some of the answers here helpful and could imagine some of them as good "tactical advice" to go along with cultural norms. I also ended up looking through Google's SRE book as recommended in Gwern's answer and benefited from it even though it's focused on software infrastructure. In particular, the idea of treating knowledge production as a complex system helped knock me out of my "just be careful" mindset, which I think is often one of the harder things to scale. Of course, YMMV.
As each new writing deadline approaches, I return to The Builders, a poem by Longfellow:
Each year the exhortation to Let us do our work as well becomes harder to follow, both because the pace of ML keeps increasing, and because more students show up to my lab so my attention is split between more projects. This creates pressure to triage and to leave some broken stairways as they are.
I'm sure that I inevitably do leave some stairways broken--questioning experiments in less detail than I would have as a PhD student, or leaving weak or ambiguous arguments in a paper because we ran out of time before the deadline. That being said, I strongly desire to build a culture that is worthy of the elder days of Art.
Is it possible to do this without permanently sacrificing scalability? I think yes, for the following reasons:
Perhaps the common thread is that scaling and high standards may seem at odds in the short-term, but in the longer term they are aligned: you can't scale well without also having processes in place to create consistently high-quality work.